{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"A very simple MNIST classifier.\n",
    "See extensive documentation at\n",
    "https://www.tensorflow.org/get_started/mnist/beginners\n",
    "\"\"\"\n",
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import argparse\n",
    "import sys\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "FLAGS = None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们在这里调用系统提供的Mnist数据函数为我们读入数据，如果没有下载的话则进行下载。\n",
    "\n",
    "<font color=#ff0000>**这里将data_dir改为适合你的运行环境的目录**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting /tmp/tensorflow/mnist/input_data/train-images-idx3-ubyte.gz\n",
      "Extracting /tmp/tensorflow/mnist/input_data/train-labels-idx1-ubyte.gz\n",
      "Extracting /tmp/tensorflow/mnist/input_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting /tmp/tensorflow/mnist/input_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# Import data\n",
    "data_dir = '/tmp/tensorflow/mnist/input_data'\n",
    "mnist = input_data.read_data_sets(data_dir, one_hot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一个非常非常简陋的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 声明一些参数\n",
    "batch_size = 100\n",
    "total_step = int(mnist.train.num_examples / batch_size)\n",
    "global_step = tf.Variable(0, trainable=False)\n",
    "starting_learning_rate = 0.1\n",
    "learning_rate = tf.train.exponential_decay(starting_learning_rate, global_step,\n",
    "                                           300, 0.8, staircase=False)\n",
    "training_epochs = 500\n",
    "display_step = 50\n",
    "input_nodes = 28 * 28\n",
    "output_nodes = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the model\n",
    "x = tf.placeholder(tf.float32, [None, input_nodes])\n",
    "import math\n",
    "#增加一个隐层 \n",
    "#用随机数对W1 进行初始化\n",
    "nodes1 = math.ceil(math.sqrt(mnist.train.num_examples)) # 第一层神经元个数 根据网上经验最优解大概为输入输出和的平方根\n",
    "stddev1 = 2 / math.sqrt(input_nodes + nodes1) #貌似这样比较好 根据明神的提示\n",
    "init1 = tf.truncated_normal((input_nodes,nodes1), stddev=stddev1) \n",
    "\n",
    "W1 = tf.Variable(init1, name='weight1')\n",
    "b1 = tf.Variable(tf.zeros([nodes1]))\n",
    "logits1 = tf.add(tf.matmul(x, W1), b1)\n",
    "#增加激活函数,尝试sigmoid, tanh, 和 Relu\n",
    "# y1 = tf.nn.sigmoid(logits1)\n",
    "# y1 = tf.nn.tanh(logits1)\n",
    "y1 = tf.nn.relu(logits1)\n",
    "\n",
    "#再增加一个隐层 貌似再加一个隐层效果也不好 先不加了\n",
    "# nodes2 = nodes1\n",
    "# init2 = tf.random_normal([nodes1, nodes2], seed=32)\n",
    "# W2 = tf.Variable(init2, name='weight2')\n",
    "# b2 = tf.Variable(tf.random_normal([nodes2])) #用随机数初始化Bias\n",
    "# logits2 = tf.add(tf.matmul(y1, W2), b2)\n",
    "# # y2 = tf.nn.relu(logits2)\n",
    "# y2 = tf.nn.sigmoid(logits1)\n",
    "\n",
    "\n",
    "init2 = tf.random_normal([nodes1, output_nodes], seed=32)\n",
    "W2 = tf.Variable(init2, name='weight2')\n",
    "b2 = tf.Variable(tf.random_normal([output_nodes]))#用随机数初始化Bias\n",
    "logits2 = tf.add(tf.matmul(y1, W2), b2)\n",
    "y = logits2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "定义我们的ground truth 占位符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define loss and optimizer\n",
    "y_ = tf.placeholder(tf.float32, [None, output_nodes])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来我们计算交叉熵，注意这里不要使用注释中的手动计算方式，而是使用系统函数。\n",
    "另一个注意点就是，softmax_cross_entropy_with_logits的logits参数是**未经激活的wx+b**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The raw formulation of cross-entropy,\n",
    "#\n",
    "#   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),\n",
    "#                                 reduction_indices=[1]))\n",
    "#\n",
    "# can be numerically unstable.\n",
    "#\n",
    "# So here we use tf.nn.softmax_cross_entropy_with_logits on the raw\n",
    "# outputs of 'y', and then average across the batch.\n",
    "cross_entropy = tf.reduce_mean(\n",
    "    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "生成一个训练step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n",
    "\n",
    "sess = tf.Session()\n",
    "init_op = tf.global_variables_initializer()\n",
    "sess.run(init_op)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这里我们仍然调用系统提供的读取数据，为我们取得一个batch。\n",
    "然后我们运行3k个step(5 epochs)，对权重进行优化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Current epoch:0, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
      "Step:0 0.2423\n",
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      "\n",
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      "Current epoch:1, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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      "\n",
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      "Current epoch:2, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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      "\n",
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      "Current epoch:3, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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      "\n",
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      "Current epoch:24, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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      "Current epoch:25, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Current epoch:265, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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      "Step:0 0.9803\n"
     ]
    },
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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      "Current epoch:305, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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      "Current epoch:306, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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      "Current epoch:310, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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      "Current epoch:312, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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      "Step:0 0.9802\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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      "Current epoch:423, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Current epoch:465, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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      "Current epoch:468, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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      "Current epoch:470, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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      "Current epoch:471, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Current epoch:495, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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      "Current epoch:498, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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      "Current epoch:499, total Step:550, learning rate:Tensor(\"ExponentialDecay_10:0\", shape=(), dtype=float32)\n",
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     ]
    }
   ],
   "source": [
    "#将训练过程中产生的accuracy\n",
    "acc = dict()\n",
    "correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "# Train\n",
    "for epoch in range(training_epochs):\n",
    "#     learning_rate *= 0.7\n",
    "    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n",
    "    for _ in range(total_step):\n",
    "        batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
    "        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n",
    "        if _ % display_step == 0:\n",
    "            acc[_] = sess.run(accuracy, feed_dict={x: mnist.test.images,\n",
    "                                          y_: mnist.test.labels})\n",
    "            if _ == 0:\n",
    "                print('\\n\\nCurrent epoch:{}, total Step:{}, learning rate:{}'.format(epoch, total_step, learning_rate))\n",
    "            print('Step:{}'.format(_),sess.run(accuracy, feed_dict={x: mnist.test.images,\n",
    "                                          y_: mnist.test.labels}))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1aceba8da0>]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SsCHKReNHcvGEkT3vnKZRw8r5n1+ex8ETp/nW45sLKoKhIdLMuJFD+P0Z6d1rIpmJhwNq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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ec1ce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#将准确率和迭代次数可视化\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# plt.plot([acc.keys],[acc.values])\n",
    "# plt.show()\n",
    "xs = []\n",
    "ys = []\n",
    "for i, value in acc.items():\n",
    "    xs.append(i)\n",
    "    ys.append(value)\n",
    "plt.plot(xs, ys)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "验证我们模型在测试数据上的准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9806\n"
     ]
    }
   ],
   "source": [
    "  # Test trained model\n",
    "correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "print(sess.run(accuracy, feed_dict={x: mnist.test.images,\n",
    "                                      y_: mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1层隐层,745个神经元 忘记为啥用这个数了\n",
    "激活函数为sigmoid函数:\n",
    "- Step = 3000, learning rate = 0.3 accuracy = 0.9082\n",
    "- Step = 6000, learning rate = 0.3 accuracy = 0.9275\n",
    "- Step = 9000, learning rate = 0.3 accuracy = 0.9317\n",
    "- Step = 20000, learning rate = 0.3 accuracy = 0.9339\n",
    "- Step = 20000, learning rate = 0.2 accuracy = 0.9361\n",
    "- Step = 20000, learning rate = 1 accuracy = 0.9491\n",
    "- Step = 20000, learning rate = 0.5 accuracy = 0.9435\n",
    "\n",
    "激活函数为tanh函数\n",
    "- Step = 3000, learning rate = 0.5 accuracy = 0.9271\n",
    "- Step = 6000, learning rate = 0.5 accuracy = 0.935\n",
    "- Step = 9000, learning rate = 0.5 accuracy = 0.9362\n",
    "- Step = 12000, learning rate = 0.5 accuracy = 0.9355\n",
    "- Step = 3000, learning rate = 0.2 accuracy = 0.9088\n",
    "- Step = 6000, learning rate = 0.2 accuracy = 0.912\n",
    "- Step = 9000, learning rate = 0.2 accuracy = 0.912\n",
    "- Step = 12000, learning rate = 0.2 accuracy = 0.9183\n",
    "- Step = 12000, learning rate = 1 accuracy = 0.9588\n",
    "\n",
    "激活函数为Relu函数\n",
    "\n",
    "## 1层隐层,245个神经元 根据经验,为输入节点和输出节点的和的平方根\n",
    "激活函数为sigmoid函数:\n",
    "- Total Step:20000, learning rate:0.5, accuracy = 0.9534\n",
    "- Total Step:20000, learning rate:0.2, accuracy = 0.9417\n",
    "- Total Step:40000, learning rate:0.2, accuracy = 0.9491\n",
    "\n",
    "激活函数为Relu函数\n",
    "- Total Step:40000, learning rate:0.2, accuracy = 0.9495\n",
    "\n",
    "\n",
    "## 后面又没有目的的随机试了一些参数,觉得效果变化不大. 故略.\n",
    "- 尝试了增加一个隐层\n",
    "- 尝试了换不同的激活函数,Relu效果不错还有改学习率和epoch数\n",
    "\n",
    "\n",
    "## 尝试用随机正态分布数做初始化,效果比较好\n",
    "- epoch: 500, learning rate start: 0.1, decay = 0.8, batch = 3000 accuracy = 0.9782\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "毫无疑问，这个模型是一个非常简陋，性能也不理想的模型。目前只能达到92%左右的准确率。\n",
    "接下来，希望大家利用现有的知识，将这个模型优化至98%以上的准确率。\n",
    "Hint：\n",
    "- 多隐层\n",
    "- 激活函数\n",
    "- 正则化\n",
    "- 初始化\n",
    "- 摸索一下各个超参数\n",
    "  - 隐层神经元数量\n",
    "  - 学习率\n",
    "  - 正则化惩罚因子\n",
    "  - 最好每隔几个step就对loss、accuracy等等进行一次输出，这样才能有根据地进行调整"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
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